Docking and Three-Dimensional Quantitative Structure−Activity Relationship (3D QSAR) Analyses of Nonsteroidal Progesterone Receptor Ligands

• Published in: Journal of medicinal chemistry Vol. 49; no. 14; pp. 4261 - 4268
• Main Authors: Söderholm, Annu A, Lehtovuori, Pekka T, Nyrönen, Tommi H
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 13.07.2006
• Subjects: Binding Sites; Biological and medical sciences; Hormones. Endocrine system; Hydrogen Bonding; Ligands; Medical sciences; Models, Molecular; Pharmacology. Drug treatments; Quantitative Structure-Activity Relationship; Quinolines - chemistry; Receptors, Progesterone - chemistry; Ligand; Prediction; Quinoline derivatives; Progesterone receptor; Modeling; Thiophene derivatives; Three dimensional model; Comparative molecular similarity indices analysis method; Structure activity relation; Molecular model; Non steroid compound; Lipophilicity; Physicochemical properties; Hydrogen bond; Hormonal receptor
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm060234e

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r 2 = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r pred 2 = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.